Movement comparison

UNIMASOFT

In a project in partnership with Unimasoft, a feasibility study should determine whether the motion comparison, performed by CDRIN-developed animation comparison tools, can be made from images captured by a simple, affordable Kinect-like quality system.

The comparison involves analyzing a newly captured motion against an optimal reference motion to measure a deviation in an acceptable range, which determines whether the new motion is correctly performed or not. Since the movements can be performed by people of different body sizes, in different orientations, at different speeds and over different animation lengths, the comparisons must be robust in relation to these factors:

– The orientation of the actor during capture.

– The body segment parameters of each individual.

– The variable duration of execution for each gesture composing the complete movement.

– The variable duration of execution of the complete movement.

– Capture errors induced by the systems themselves.

The following algorithms have been programmed and used for the project:

– Detection of inflection points : It is not necessary to compare the whole movement, some data are more characteristic than others, especially changes of direction. This makes it possible to limit the complexity of analysis and unnecessary calculation times.

– Difference of animation curves : In order to compare a motion, we need to establish the distance of this motion from a reference movement.

– Detections of capture errors: In order to overcome capture errors, it is necessary to be able to detect, at least, that the capture has been problematic (eg the rotations of body segments are physically impossible, or their acceleration is physically improbable).

– Dynamic Time Warpping (DTW) : The data recorded by the learner does not necessarily have the same time base as the reference data, and is not performed at the same speed. We need to match some remarkable points on the curves in order to analyze if the learner realizes his movement in a way similar to the reference.

– Motion Templates : The concept of classification in machine learning, in the context of movement, is often the recognition of a motion from a set of pre-established potential motions. But the classification can also be used to evaluate the distance of the movement made compared to the known movements: what numerical value can be assigned to evaluate the motion with respect to the reference movements.